{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:52.581567Z",
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     "iopub.status.idle": "2025-02-09T06:00:52.583793Z",
     "shell.execute_reply": "2025-02-09T06:00:52.583207Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.581550Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:52.584858Z",
     "iopub.status.busy": "2025-02-09T06:00:52.584597Z",
     "iopub.status.idle": "2025-02-09T06:00:52.592081Z",
     "shell.execute_reply": "2025-02-09T06:00:52.591549Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.584821Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:52.593157Z",
     "iopub.status.busy": "2025-02-09T06:00:52.592788Z",
     "iopub.status.idle": "2025-02-09T06:00:52.595449Z",
     "shell.execute_reply": "2025-02-09T06:00:52.594888Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.593130Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-09T06:00:52.619341Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.596455Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1195702.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:52.620888Z",
     "iopub.status.busy": "2025-02-09T06:00:52.620632Z",
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     "shell.execute_reply": "2025-02-09T06:00:52.629093Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.620863Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
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     "iopub.status.busy": "2025-02-09T06:00:52.631811Z",
     "iopub.status.idle": "2025-02-09T06:00:52.634204Z",
     "shell.execute_reply": "2025-02-09T06:00:52.633632Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.632075Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:52.635211Z",
     "iopub.status.busy": "2025-02-09T06:00:52.634968Z",
     "iopub.status.idle": "2025-02-09T06:00:52.640826Z",
     "shell.execute_reply": "2025-02-09T06:00:52.640310Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.635187Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:52.800702Z",
     "iopub.status.busy": "2025-02-09T06:00:52.800409Z",
     "iopub.status.idle": "2025-02-09T06:00:52.880914Z",
     "shell.execute_reply": "2025-02-09T06:00:52.880319Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.800675Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:52.882144Z",
     "iopub.status.busy": "2025-02-09T06:00:52.881680Z",
     "iopub.status.idle": "2025-02-09T06:00:52.888283Z",
     "shell.execute_reply": "2025-02-09T06:00:52.887814Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.882116Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:52.889244Z",
     "iopub.status.busy": "2025-02-09T06:00:52.888863Z",
     "iopub.status.idle": "2025-02-09T06:00:52.894176Z",
     "shell.execute_reply": "2025-02-09T06:00:52.893719Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.889216Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.2989, 0.6096, 0.0915],\n",
      "        [0.6911, 0.1646, 0.1443]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:52.894980Z",
     "iopub.status.busy": "2025-02-09T06:00:52.894802Z",
     "iopub.status.idle": "2025-02-09T06:00:53.093971Z",
     "shell.execute_reply": "2025-02-09T06:00:53.093359Z",
     "shell.execute_reply.started": "2025-02-09T06:00:52.894962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.094752Z",
     "iopub.status.busy": "2025-02-09T06:00:53.094541Z",
     "iopub.status.idle": "2025-02-09T06:00:53.297339Z",
     "shell.execute_reply": "2025-02-09T06:00:53.296875Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.094736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.298140Z",
     "iopub.status.busy": "2025-02-09T06:00:53.297973Z",
     "iopub.status.idle": "2025-02-09T06:00:53.306347Z",
     "shell.execute_reply": "2025-02-09T06:00:53.305738Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.298124Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.307144Z",
     "iopub.status.busy": "2025-02-09T06:00:53.306943Z",
     "iopub.status.idle": "2025-02-09T06:00:53.313738Z",
     "shell.execute_reply": "2025-02-09T06:00:53.313279Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.307121Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.314777Z",
     "iopub.status.busy": "2025-02-09T06:00:53.314534Z",
     "iopub.status.idle": "2025-02-09T06:00:53.322225Z",
     "shell.execute_reply": "2025-02-09T06:00:53.321650Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.314755Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.326267Z",
     "iopub.status.busy": "2025-02-09T06:00:53.325820Z",
     "iopub.status.idle": "2025-02-09T06:00:53.331155Z",
     "shell.execute_reply": "2025-02-09T06:00:53.330659Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.326244Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.331813Z",
     "iopub.status.busy": "2025-02-09T06:00:53.331645Z",
     "iopub.status.idle": "2025-02-09T06:00:53.458039Z",
     "shell.execute_reply": "2025-02-09T06:00:53.457564Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.331795Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.458813Z",
     "iopub.status.busy": "2025-02-09T06:00:53.458634Z",
     "iopub.status.idle": "2025-02-09T06:00:53.870211Z",
     "shell.execute_reply": "2025-02-09T06:00:53.869661Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.458797Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.871063Z",
     "iopub.status.busy": "2025-02-09T06:00:53.870875Z",
     "iopub.status.idle": "2025-02-09T06:00:53.885961Z",
     "shell.execute_reply": "2025-02-09T06:00:53.885459Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.871046Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.886814Z",
     "iopub.status.busy": "2025-02-09T06:00:53.886476Z",
     "iopub.status.idle": "2025-02-09T06:00:53.890622Z",
     "shell.execute_reply": "2025-02-09T06:00:53.890135Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.886798Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.891293Z",
     "iopub.status.busy": "2025-02-09T06:00:53.891155Z",
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     "shell.execute_reply": "2025-02-09T06:00:53.894174Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.891280Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.895506Z",
     "iopub.status.busy": "2025-02-09T06:00:53.895224Z",
     "iopub.status.idle": "2025-02-09T06:00:53.982895Z",
     "shell.execute_reply": "2025-02-09T06:00:53.982234Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.895491Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.983578Z",
     "iopub.status.busy": "2025-02-09T06:00:53.983430Z",
     "iopub.status.idle": "2025-02-09T06:00:53.986970Z",
     "shell.execute_reply": "2025-02-09T06:00:53.986466Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.983564Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.987631Z",
     "iopub.status.busy": "2025-02-09T06:00:53.987445Z",
     "iopub.status.idle": "2025-02-09T06:00:53.992000Z",
     "shell.execute_reply": "2025-02-09T06:00:53.991516Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.987617Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"test_2.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.992565Z",
     "iopub.status.busy": "2025-02-09T06:00:53.992425Z",
     "iopub.status.idle": "2025-02-09T06:00:53.996133Z",
     "shell.execute_reply": "2025-02-09T06:00:53.995637Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.992551Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:53.996702Z",
     "iopub.status.busy": "2025-02-09T06:00:53.996563Z",
     "iopub.status.idle": "2025-02-09T06:00:54.000307Z",
     "shell.execute_reply": "2025-02-09T06:00:53.999790Z",
     "shell.execute_reply.started": "2025-02-09T06:00:53.996688Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:54.001109Z",
     "iopub.status.busy": "2025-02-09T06:00:54.000944Z",
     "iopub.status.idle": "2025-02-09T06:00:54.007596Z",
     "shell.execute_reply": "2025-02-09T06:00:54.007169Z",
     "shell.execute_reply.started": "2025-02-09T06:00:54.001094Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:54.008451Z",
     "iopub.status.busy": "2025-02-09T06:00:54.008086Z",
     "iopub.status.idle": "2025-02-09T06:00:54.166961Z",
     "shell.execute_reply": "2025-02-09T06:00:54.166524Z",
     "shell.execute_reply.started": "2025-02-09T06:00:54.008437Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 1024, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.2, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 4, # 可调整\n",
    "    \"num_decoder_layers\": 4, # 可调整\n",
    "    \"num_encoder_layers\": 4, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": True,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 4096\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:54.167676Z",
     "iopub.status.busy": "2025-02-09T06:00:54.167456Z",
     "iopub.status.idle": "2025-02-09T06:00:55.168899Z",
     "shell.execute_reply": "2025-02-09T06:00:55.168419Z",
     "shell.execute_reply.started": "2025-02-09T06:00:54.167660Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 102497280\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:00:55.169641Z",
     "iopub.status.busy": "2025-02-09T06:00:55.169429Z",
     "iopub.status.idle": "2025-02-09T06:37:52.480032Z",
     "shell.execute_reply": "2025-02-09T06:37:52.479578Z",
     "shell.execute_reply.started": "2025-02-09T06:00:55.169625Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "27999it [36:55, 12.64it/s, epoch=52, loss=2.37, val_loss=2.85]                      "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 53 / global_step 28000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5a2abb09-110b-42ce-bbc1-126d250cfde7",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T06:41:51.784628Z",
     "iopub.status.busy": "2025-02-09T06:41:51.784322Z",
     "iopub.status.idle": "2025-02-09T06:41:51.789246Z",
     "shell.execute_reply": "2025-02-09T06:41:51.788832Z",
     "shell.execute_reply.started": "2025-02-09T06:41:51.784608Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'loss': 2.1691810044689457, 'step': 27971},\n",
       " {'loss': 2.5009465029365137, 'step': 27972},\n",
       " {'loss': 2.4473972701550126, 'step': 27973},\n",
       " {'loss': 2.2350951117904563, 'step': 27974},\n",
       " {'loss': 2.394904074733431, 'step': 27975},\n",
       " {'loss': 2.2761815454775474, 'step': 27976},\n",
       " {'loss': 1.9011660665273666, 'step': 27977},\n",
       " {'loss': 1.9898636150691245, 'step': 27978},\n",
       " {'loss': 2.1782186282589833, 'step': 27979},\n",
       " {'loss': 2.144512479365426, 'step': 27980},\n",
       " {'loss': 2.0879982232197003, 'step': 27981},\n",
       " {'loss': 2.3651765530424744, 'step': 27982},\n",
       " {'loss': 1.9752182880072138, 'step': 27983},\n",
       " {'loss': 1.9982326009367528, 'step': 27984},\n",
       " {'loss': 2.3353637098812783, 'step': 27985},\n",
       " {'loss': 2.0335226301507, 'step': 27986},\n",
       " {'loss': 1.9913023817662474, 'step': 27987},\n",
       " {'loss': 2.1301234121808035, 'step': 27988},\n",
       " {'loss': 2.139562304404643, 'step': 27989},\n",
       " {'loss': 2.305049363363104, 'step': 27990},\n",
       " {'loss': 2.1323936808749537, 'step': 27991},\n",
       " {'loss': 2.13560700113188, 'step': 27992},\n",
       " {'loss': 2.353109395676765, 'step': 27993},\n",
       " {'loss': 2.007578304319671, 'step': 27994},\n",
       " {'loss': 1.8792593843190104, 'step': 27995},\n",
       " {'loss': 2.378340117973194, 'step': 27996},\n",
       " {'loss': 2.26820767932647, 'step': 27997},\n",
       " {'loss': 2.128117891096733, 'step': 27998},\n",
       " {'loss': 2.2282723431723026, 'step': 27999},\n",
       " {'loss': 2.2992994213780613, 'step': 28000}]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_dict[\"train\"][-30:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-09T06:37:52.481175Z",
     "iopub.status.busy": "2025-02-09T06:37:52.480706Z",
     "iopub.status.idle": "2025-02-09T06:37:52.665795Z",
     "shell.execute_reply": "2025-02-09T06:37:52.665337Z",
     "shell.execute_reply.started": "2025-02-09T06:37:52.481157Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/test_2.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "6f512e7c8f2db972",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "28it [00:00, 133.72it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:06, 147.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 2.8072235568216377\n",
      "testing bleu: 0.7032844156629026\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
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   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2025-02-09T06:38:00.465125Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
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   "id": "c5d288cf83d67b62",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  8%|▊         | 10/128 [00:00<00:02, 58.54it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['a man and woman eating food in the evening.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    # \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake. ans:['man in a white boat on a boat.']\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach. ans:['a man with a girl and a girl on the beach with a fishing pole.']\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race. ans:['three men on horses are running in a race.']\n",
    "    \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐 # ['a man and a woman are eating food.']\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
